{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "humanist_vols = pd.read_csv('web_scraped_humanist_listserv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "film_scripts = pd.read_csv('pudding_film_scripts.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dates    object\n",
       "text     object\n",
       "url      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humanist_vols.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "imdb_id                        object\n",
       "script_id                       int64\n",
       "title                          object\n",
       "year                            int64\n",
       "gross (inflation-adjusted)    float64\n",
       "link                           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "film_scripts.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dates</th>\n",
       "      <th>text</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1987-1988</td>\n",
       "      <td>From: MCCARTY@UTOREPAS\\nSubject: \\nDate: 12 Ma...</td>\n",
       "      <td>https://humanist.kdl.kcl.ac.uk/Archives/Conver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988-1989</td>\n",
       "      <td>From: Sebastian Rahtz \\nSubject: C++ and Gnu o...</td>\n",
       "      <td>https://humanist.kdl.kcl.ac.uk/Archives/Conver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989-1990</td>\n",
       "      <td>From: Willard McCarty \\nSubject: Happy Birthda...</td>\n",
       "      <td>https://humanist.kdl.kcl.ac.uk/Archives/Conver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-1991</td>\n",
       "      <td>From: Elaine Brennan &amp; Allen Renear \\nSubject:...</td>\n",
       "      <td>https://humanist.kdl.kcl.ac.uk/Archives/Conver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1991-1992</td>\n",
       "      <td>From: Elaine Brennan &amp; Allen Renear \\nSubject:...</td>\n",
       "      <td>https://humanist.kdl.kcl.ac.uk/Archives/Conver...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dates                                               text  \\\n",
       "0  1987-1988  From: MCCARTY@UTOREPAS\\nSubject: \\nDate: 12 Ma...   \n",
       "1  1988-1989  From: Sebastian Rahtz \\nSubject: C++ and Gnu o...   \n",
       "2  1989-1990  From: Willard McCarty \\nSubject: Happy Birthda...   \n",
       "3  1990-1991  From: Elaine Brennan & Allen Renear \\nSubject:...   \n",
       "4  1991-1992  From: Elaine Brennan & Allen Renear \\nSubject:...   \n",
       "\n",
       "                                                 url  \n",
       "0  https://humanist.kdl.kcl.ac.uk/Archives/Conver...  \n",
       "1  https://humanist.kdl.kcl.ac.uk/Archives/Conver...  \n",
       "2  https://humanist.kdl.kcl.ac.uk/Archives/Conver...  \n",
       "3  https://humanist.kdl.kcl.ac.uk/Archives/Conver...  \n",
       "4  https://humanist.kdl.kcl.ac.uk/Archives/Conver...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humanist_vols.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace and contains\n",
    "#df[‘column_name’].str.contains(‘pattern’)\n",
    "# humanist_vols[(humanist_vols['dates'].str.contains('20') == True) &(humanist_vols['dates'].str.contains('19') == False)] \n",
    "\n",
    "# df[‘column_name’].str.replace(‘old_string’, ‘new_string’)\n",
    "\n",
    "humanist_vols['cleaned_dates'] = humanist_vols['dates'].str.replace('-', '/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'test2'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'test_test2'.split('_')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [1987, 1988]\n",
       "1     [1988, 1989]\n",
       "2     [1989, 1990]\n",
       "3     [1990, 1991]\n",
       "4     [1991, 1992]\n",
       "5     [1992, 1993]\n",
       "6     [1993, 1994]\n",
       "7     [1994, 1995]\n",
       "8     [1995, 1996]\n",
       "9     [1996, 1997]\n",
       "10    [1997, 1998]\n",
       "11    [1998, 1999]\n",
       "12    [1999, 2000]\n",
       "13    [2000, 2001]\n",
       "14    [2001, 2002]\n",
       "15    [2002, 2003]\n",
       "16    [2003, 2004]\n",
       "17    [2004, 2005]\n",
       "18    [2005, 2006]\n",
       "19    [2006, 2007]\n",
       "20    [2007, 2008]\n",
       "Name: cleaned_dates, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humanist_vols['cleaned_dates'].str.split('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "humanist_vols['year_start'] = humanist_vols['cleaned_dates'].str.split('/').str[0]\n",
    "humanist_vols['year_end'] = humanist_vols['cleaned_dates'].str.split('/').str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "humanist_vols['volume_size'] = humanist_vols['text'].str.count('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year_start'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "humanist_vols.plot(x='year_start', y='volume_size', kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year_start'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "humanist_vols.plot(x='year_start', y='volume_size', kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "humanist_vols['humanities_computing_counts'] = humanist_vols['text'].str.count('Humanities Computing')\n",
    "humanist_vols['digital_humanities_counts'] = humanist_vols['text'].str.count('Digital Humanities')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "humanist_vols[['humanities_computing_counts', 'digital_humanities_counts']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk import FreqDist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['From',\n",
       " ':',\n",
       " 'MCCARTY',\n",
       " '@',\n",
       " 'UTOREPAS',\n",
       " 'Subject',\n",
       " ':',\n",
       " 'Date',\n",
       " ':',\n",
       " '12',\n",
       " 'May',\n",
       " '1987',\n",
       " ',',\n",
       " '23:50:02',\n",
       " 'EDT',\n",
       " 'X-Humanist',\n",
       " ':',\n",
       " 'Vol',\n",
       " '.',\n",
       " '1',\n",
       " 'Num',\n",
       " '.',\n",
       " '1',\n",
       " '(',\n",
       " '1',\n",
       " ')',\n",
       " 'This',\n",
       " 'is',\n",
       " 'test',\n",
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       " 'person',\n",
       " 'from',\n",
       " 'each',\n",
       " 'to',\n",
       " 'send',\n",
       " 'a',\n",
       " 'test',\n",
       " 'message',\n",
       " '.',\n",
       " 'This',\n",
       " 'will',\n",
       " 'mean',\n",
       " ',',\n",
       " 'alas',\n",
       " ',',\n",
       " 'more',\n",
       " 'junk-mail',\n",
       " ',',\n",
       " 'which',\n",
       " 'I',\n",
       " 'hope',\n",
       " 'you',\n",
       " 'will',\n",
       " 'excuse',\n",
       " '.',\n",
       " 'From',\n",
       " ':',\n",
       " 'SUE',\n",
       " 'ZAYAC',\n",
       " 'Subject',\n",
       " ':',\n",
       " 'Scholarly',\n",
       " 'Information',\n",
       " 'Journal',\n",
       " 'Date',\n",
       " ':',\n",
       " 'Mon',\n",
       " ',',\n",
       " '18',\n",
       " 'May',\n",
       " '87',\n",
       " '09:51',\n",
       " 'EDT',\n",
       " 'X-Humanist',\n",
       " ':',\n",
       " 'Vol',\n",
       " '.',\n",
       " '1',\n",
       " 'Num',\n",
       " '.',\n",
       " '9',\n",
       " '(',\n",
       " '9',\n",
       " ')',\n",
       " 'Hello',\n",
       " ':',\n",
       " 'I',\n",
       " \"'ve\",\n",
       " 'just',\n",
       " 'sent',\n",
       " 'a',\n",
       " 'complimentary',\n",
       " 'copy',\n",
       " 'of',\n",
       " 'the',\n",
       " 'Columbia',\n",
       " 'University',\n",
       " \"''\",\n",
       " 'Scholarly',\n",
       " 'Information',\n",
       " 'Center',\n",
       " 'Journal',\n",
       " \"''\",\n",
       " 'to',\n",
       " 'all',\n",
       " 'of',\n",
       " 'you',\n",
       " 'whose',\n",
       " 'names',\n",
       " 'I',\n",
       " 'had',\n",
       " 'from',\n",
       " 'the',\n",
       " 'original',\n",
       " 'meeting',\n",
       " 'of',\n",
       " 'the',\n",
       " 'Ad',\n",
       " 'Hoc',\n",
       " 'SIC',\n",
       " 'for',\n",
       " 'Support',\n",
       " 'Issues',\n",
       " 'at',\n",
       " 'USC',\n",
       " 'in',\n",
       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(humanist_vols[0:2]['text'].map(word_tokenize), [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = FreqDist(sum(humanist_vols[0:2]['text'].map(word_tokenize), []))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens.plot(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "porter = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "subset_humanist_vols = humanist_vols[0:2]\n",
    "\n",
    "def stem_words(row):\n",
    "    stemmed_words = ''\n",
    "    for token in row.split(' '):\n",
    "        stemmed_words += porter.stem(token) + ' '\n",
    "    return stemmed_words\n",
    "\n",
    "subset_humanist_vols['stemmed_text'] = subset_humanist_vols.text.apply(stem_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(subset_humanist_vols[0:1]['stemmed_text'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatize_words(row):\n",
    "    #Code goes here\n",
    "\n",
    "subset_humanist_vols.apply(lemmatize_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "\n",
    "#save our texts to a list\n",
    "documents = humanist_vols.text.tolist()\n",
    "\n",
    "#Create a vectorizer\n",
    "vectorizer = TfidfVectorizer(max_df=0.9, min_df=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/Users/EZCorp/Sites/IS310/is310_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Fit the vectorizer to our emails\n",
    "transformed_documents = vectorizer.fit_transform(documents)\n",
    "\n",
    "# Now get the top features for each document\n",
    "transformed_documents_as_array = transformed_documents.toarray()\n",
    "\n",
    "dates = humanist_vols.dates.tolist()\n",
    "tfidf_results = []\n",
    "for counter, doc in enumerate(transformed_documents_as_array):\n",
    "    # construct a dataframe\n",
    "    tf_idf_tuples = list(zip(vectorizer.get_feature_names(), doc))\n",
    "    one_doc_as_df = pd.DataFrame.from_records(tf_idf_tuples, columns=['term', 'score']).sort_values(by='score', ascending=False).reset_index(drop=True)\n",
    "    one_doc_as_df['dates'] = dates[counter]\n",
    "    tfidf_results.append(one_doc_as_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>score</th>\n",
       "      <th>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.704104</td>\n",
       "      <td>2000-2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.702940</td>\n",
       "      <td>1999-2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.688043</td>\n",
       "      <td>1998-1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.651086</td>\n",
       "      <td>1997-1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>utorepas</td>\n",
       "      <td>0.648617</td>\n",
       "      <td>1987-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.624448</td>\n",
       "      <td>2001-2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.605409</td>\n",
       "      <td>2007-2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.595785</td>\n",
       "      <td>2004-2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http</td>\n",
       "      <td>0.590306</td>\n",
       "      <td>2003-2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bitnet</td>\n",
       "      <td>0.587312</td>\n",
       "      <td>1989-1990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       term     score      dates\n",
       "0      http  0.704104  2000-2001\n",
       "0      http  0.702940  1999-2000\n",
       "0      http  0.688043  1998-1999\n",
       "0      http  0.651086  1997-1998\n",
       "0  utorepas  0.648617  1987-1988\n",
       "0      http  0.624448  2001-2002\n",
       "0      http  0.605409  2007-2008\n",
       "0      http  0.595785  2004-2005\n",
       "0      http  0.590306  2003-2004\n",
       "0    bitnet  0.587312  1989-1990"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_df = pd.concat(tfidf_results)\n",
    "tfidf_df = tfidf_df.sort_values(by=['score'], ascending=False)\n",
    "tfidf_df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['http' 'utorepas' 'bitnet' 'www' '2007' '1997' '2006' '2004' '1996'\n",
      " '2005' '2002' '2003' 'ninch' 'gopher' 'html' '1998' '2008' '1999' 'astra'\n",
      " 'ippe' 'vax' 'saddam' 'uottawa' 'doi' 'prolog' 'qs' 'amico' 'kraft' 'cdt'\n",
      " 'acadvm1' '1007' 'hussein' 'penndrls' 'fqs' '441495' 'kessler' 'cst' 'na'\n",
      " 'celia' 'epas' 'tlg' 'brownvm' 'neach' 'carf' 'forks' 'werman' 'xxx'\n",
      " 'wmccarty' 'rahtz' 'htm' 'unicode' 'dfl' 'mellen' 'ocp' 'sanskrit'\n",
      " 'kevitt' 'pacling' 'bene' 'waxweb' 'iraq' 'missile' 'idbsu' 'earn'\n",
      " 'wikipedia' 'easi' 'vspace' 'pali' 'ruhc' 'ubiquity' 'kurzweil' 'nota'\n",
      " 'coombs' 'kleio' 'iee' 'url' 'google' 'cti' 'kentvm' 'giampapa' 'rs'\n",
      " 'hurd' 'aaai' 'hypercard' 'cont' 'gas' 'chiba' 'neder' 'snobol' '3dx'\n",
      " 'mcswain' 'strangelove' 'fonorola' 'texpert' 'pdt' 'cdn' 'preprints'\n",
      " 'ccat' 'mainframe' 'mccarty_at_kcl' 'earli']\n"
     ]
    }
   ],
   "source": [
    "print(tfidf_df[0:200].term.unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1310\n",
       "1      504\n",
       "2     2355\n",
       "3     1319\n",
       "4      851\n",
       "5      592\n",
       "6      604\n",
       "7      287\n",
       "8      525\n",
       "9      389\n",
       "10     541\n",
       "11     343\n",
       "12     341\n",
       "13     486\n",
       "14     374\n",
       "15     288\n",
       "16     391\n",
       "17     227\n",
       "18     264\n",
       "19     233\n",
       "20     229\n",
       "Name: text, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humanist_vols.text.str.count('computer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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